The assertion is especially noteworthy because banks are awash in data, both big and small. The growing complexity of transactions, volatility of markets, multiplication of devices, spread of digitisation, multiplication of channels, demands from regulators—all have increased the sheer volume of data exponentially. Yet there is not enough to support robust risk management in terms of improving risk management outcomes. And when it comes to better outcomes—the ability to avoid or reduce losses stemming from a variety of risks—what counts is finding data that reveals insights and leads to action.

Banks need to make the connection between data acquisition, analysis and action. Insight is always useful. But without action, it doesn’t lead to changes in behaviour. The survey confirms that bankers are having a difficult time with this critical aspect: Four in ten (38%) say that they face difficulties in deriving actionable information from existing risk data.

“Two problems come up again and again with retail bank data. First, it’s historical. And second, it’s incomplete,” says Ozgur Kan, leader of Berkeley Research Group’s credit analytics practice and

former head of GE Capital’s credit methodology function. “Let’s say you bought a house in 2011. You applied for a mortgage. You told the bank what you owned and what you owed, gave them your pay stubs, and all the rest. Are you employed in 2014? Did you sell your car to pay gambling debts? They don’t know. ”

The third-biggest challenge to improving risk management outcomes is closely related to the first two: 37% of bankers say they can’t predict where the biggest return will occur when they need to decide where to invest in risk management. The world of analytics is clearly in flux, and the investor site AngelList.com catalogs several thousand analytics startups. Additionally, new analytics approaches pop up every day. It’s hard to know where to devote resources when there appear to be so many viable alternatives.

Not enough relevant data, difficulty moving from data to action, and the inability to know which actions will yield the biggest return: As the survey reflected, these are the three biggest challenges to improving risk-management outcomes. But given this increasingly complex landscape, what are the potential solutions?

The fourth V is value

Big data is often characterized by the so-called “three Vs” of volume, velocity and variety. But this terminology may obscure a fourth V—value. Some dimensions of data have more value than others. This value doesn’t come only from volume, as the survey participants recognized. Velocity is important in many situations, such as detecting and blocking fraudulent transactions almost instantly. The dimension that may bring the most value to the search for improved outcomes is actually variety: the ability to support decisions with previously untapped forms of data.

“The variety piece is most interesting to me,” says Mr Thomas. “We’ve always been able to store a lot of data. But now we have the opportunity to really expand the variety of data that we draw on.” For instance, Mr Thomas points to the ability to record, store and perform text analytics— statistical techniques to model the information contained in text—for every conversation between the bank and its customers. In the past, many conversations were recorded because regulators required it. But storage costs have now decreased so significantly there is no good reason not to record all customer interactions.

If traditional transaction and counterparty information comprises most of the data used now, unstructured data—text, audio, images or any data that does not fit into a traditional database structure –represents a source of additional intelligence. According to a 2012 study by research firm IDC, virtually all of the growth in data volume comes from unstructured data, and only 0.5% of the world’s data is currently analysed. Unstructured data relevant to bank risk management could be images of fingerprints, legal text from mortgage documents, or a raw feed of location data from a mobile device. All have the potential to provide new insights, but all have been largely overlooked until recently, as none come in a form ready to incorporate into an analytics application.

Benefits of a centralised approach

Just over a third of the executives surveyed rated their banks as “well above average” in identifying and analyzing risk. These banks are different from the broader survey population: Over half have centralised analytics teams or centres of excellence that develop best practices for their organisations, part of a broader trend in enterprise risk management across all industries. The selfidentified high performers are moving towards centralised approaches.

Most bankers say that the single action that could most improve risk management outcomes is “the creation of an enterprise-wide framework for stakeholders to achieve holistic perspective of all risks confronting the organisation.” Managers with a holistic perspective understand how each part of the business—and specifically the part that they’re responsible for—contributes to the risk of the whole. Centralised analytics groups can contribute to this goal, but only to the extent that they understand the business impact of risks throughout the organisation and simultaneously build relationships with individual owners of business lines within the bank.

“We are seeing a new generation of risk leaders,” says Justin Cerilli, a recruiter at Russell Reynolds Associates specialising in technology, operations, and digital transformation at large financial institutions. “They aren’t necessarily hands-on on the data side. And they aren’t necessarily quants, as quantitative skills have been commoditised to some extent. Instead, they get the business impact of risk. And they can build relationships across the organisation to get people to take action about risk. Every conversation starts with a discussion about risk, but it ends with a discussion about leadership and change management.”